Country-based modelling of COVID-19 case fatality rate:A multiple regression analysis

BACKGROUND The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection.The determinants of mortality on a global scale cannot be fully understood due to lack of information.AIM To identify key factors that may explain the variability in case lethality across countries.METHODS We identified 21 Potential risk factors for coronavirus disease 2019(COVID-19)case fatality rate for all the countries with available data.We examined univariate relationships of each variable with case fatality rate(CFR),and all independent variables to identify candidate variables for our final multiple model.Multiple regression analysis technique was used to assess the strength of relationship.RESULTS The mean of COVID-19 mortality was 1.52±1.72%.There was a statistically significant inverse correlation between health expenditure,and number of computed tomography scanners per 1 million with CFR,and significant direct correlation was found between literacy,and air pollution with CFR.This final model can predict approximately 97%of the changes in CFR.CONCLUSION The current study recommends some new predictors explaining affect mortality rate.Thus,it could help decision-makers develop health policies to fight COVID-19.

Exact controllability of rational expectations model with multiplicative noise and input delay

This paper considers the rational expectations model with multiplicative noise and input delay,where the system dynamics rely on the conditional expectations of future states.The main contribution is to obtain a sufficient condition for the exact controllability of the rational expectations model.In particular,we derive a sufficient Gramian matrix condition and a rank condition for the delay-free case.The key is the solvability of the backward stochastic difference equations with input delay which is derived from the forward and backward stochastic system.

Evaluation of Productive Plant Landscapes in Cold Regions Based on a Multiple Cropping Model

Four varieties of each rapeseed and buckwheat were planted in different sowing periods to explore a variety of planting patterns.A theoretical foundation was provided for the innovative application of cold region productive plant landscapes.The analytic hierarchy process was employed to develop a model for the evaluation of multiple cropping systems.A comprehensive evaluation was conducted to study 10 indicators in plant type,flower color,flowering period,flower volume,branch coverage,plot average yield,number of grains per plant,yield per plant,thousand-grain quality and ecological adaptability in four different varieties of each rapeseed and buckwheat.The results indicated that flower color,ecological adaptability,plot average yield and flower volume were the most important indicators for the value of productive plant landscapes in cold regions.Concerning the sowing period,the optimal combination of varieties and planting times were March 31 for Qingza No.5(rapeseed)and July 18 for Xinong T1211(buckwheat).

Modeling continuous traffic flow with the average velocity effect of multiple vehicles ahead on gyroidal roads

In the future connected vehicle environment,the information of multiple vehicles ahead can be readily collected in real-time,such as the velocity or headway,which provides more opportunities for information exchange and cooperative control.Meanwhile,gyroidal roads are one of the fundamental road patterns prevalent in mountainous areas.To effectively control the system,it is therefore significant to explore the evolution mechanism of traffic flow on gyroidal roads under a connected vehicle environment.In this paper,we present a new continuum model with the average velocity of multiple vehicles ahead on gyroidal roads.The stability criterion and KdV-Burger equation are deduced via linear and nonlinear stability analysis,respectively.Solving the above KdV-Burger equation yields the density wave solution,which explores the formation and propagation property of traffic jams near the neutral stability curve.Simulation examples verify that the model can reproduce complex phenomena,such as shock waves and rarefaction waves.The analysis of the local cluster effect shows that the number of vehicles ahead and the radius information,and the slope information of gyroidal roads can exert a great influence on traffic jams.The effect of the first and second terms are positive,while the last term is negative.

农户经济视角下的我国农村土地流转影响因素——基于Multiple Choice Model的调查研究

利用农户土地流转问卷调查所得到的微观数据,采用统计分析、Probit multiple choicemodel方法,将农户经济行为中的保障性行为、投资行为、就业行为和产业选择行为引入土地流转的实证研究领域。研究结果显示:目前的土地流转多为暂时性流转,土地流转结构性供需矛盾严重;农户对土地的需求目的更多来自于保障性需要;农户的投资行为、就业行为、社会保障行为对土地流转有着重要影响。

Improved cat swarm optimization for parameter estimation of mixed additive and multiplicative random error model

To estimate the parameters of the mixed additive and multiplicative(MAM)random error model using the weighted least squares iterative algorithm that requires derivation of the complex weight array,we introduce a derivative-free cat swarm optimization for parameter estimation.We embed the Powell method,which uses conjugate direction acceleration and does not need to derive the objective function,into the original cat swarm optimization to accelerate its convergence speed and search accuracy.We use the ordinary least squares,weighted least squares,original cat swarm optimization,particle swarm algorithm and improved cat swarm optimization to estimate the parameters of the straight-line fitting MAM model with lower nonlinearity and the DEM MAM model with higher nonlinearity,respectively.The experimental results show that the improved cat swarm optimization has faster convergence speed,higher search accuracy,and better stability than the original cat swarm optimization and the particle swarm algorithm.At the same time,the improved cat swarm optimization can obtain results consistent with the weighted least squares method based on the objective function only while avoiding multiple complex weight array derivations.The method in this paper provides a new idea for theoretical research on parameter estimation of MAM error models.

Retrieving chlorophyll content and equivalent water thickness of Moso bamboo(Phyllostachys pubescens) forests under Pantana phyllostachysae Chao-induced stress from Sentinel-2A/B images in a multiple LUTs-based PROSAIL framework

Biochemical components of Moso bamboo(Phyllostachys pubescens)are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem.The coupled PROSPECT with SAIL(PROSAIL)radiative transfer model is widely used for vegetation biochemical component content inversion.However,the presence of leaf-eating pests,such as Pantana phyllostachysae Chao(PPC),weakens the performance of the model for estimating biochemical components of Moso bamboo and thus must be considered.Therefore,this study considered pest-induced stress signals associated with Sentinel-2A/B images and field data and established multiple sets of biochemical canopy reflectance look-up tables(LUTs)based on the PROSAIL framework by setting different parameter ranges according to infestation levels.Quantitative inversions of leaf area index(LAI),leaf chlorophyll content(LCC),and leaf equivalent water thickness(LEWT)were derived.The scale conversions from LCC to canopy chlorophyll content(CCC)and LEWT to canopy equivalent water thickness(CEWT)were calculated.The results showed that LAI,CCC,and CEWT were inversely related with PPC-induced stress.When applying multiple LUTs,the p-values were<0.01;the R2 values for LAI,CCC,and CEWT were 0.71,0.68,and 0.65 with root mean square error(RMSE)(normalized RMSE,NRMSE)values of 0.38(0.16),17.56μg cm-2(0.20),and 0.02 cm(0.51),respectively.Compared to the values obtained for the traditional PROSAIL model,for October,R2 values increased by 0.05 and 0.10 and NRMSE decreased by 0.09 and 0.02 for CCC and CEWT,respectively and RMSE decreased by 0.35μg cm-2 for CCC.The feasibility of the inverse strategy for integrating pest-induced stress factors into the PROSAIL model,while establishing multiple LUTs under different pest-induced damage levels,was successfully demonstrated and can potentially enhance future vegetation parameter inversion and monitoring of bamboo forest health and ecosystems.

Combined model based on optimized multi-variable grey model and multiple linear regression

The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM（1,2） with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction.

Nonlinear Decoupling PID Control Using Neural Networks and Multiple Models

For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential （PID） controller is proposed, which consists of a traditional PID controller, a decoupling compensator and a feedforward compensator for the unmodeled dynamics. The parameters of such controller is selected based on the generalized minimum variance control law. The unmodeled dynamics is estimated and compensated by neural networks, a switching mechanism is introduced to improve tracking performance, then a nonlinear decoupling PID control algorithm is proposed. All signals in such switching system are globally bounded and the tracking error is convergent. Simulations show effectiveness of the algorithm.

Equivalent series system to model a multiple friction pendulum system with numerous sliding interfaces for seismic analyses

Current structural analysis software programs offer few if any applicable device-specifi c hysteresis rules or nonlinear elements to simulate the precise mechanical behavior of a multiple friction pendulum system(MFPS) with numerous sliding interfaces.Based on the concept of subsystems,an equivalent series system that adopts existing nonlinear elements with parameters systematically calculated and mathematically proven through rigorous derivations is proposed.The aim is to simulate the characteristics of sliding motions for an MFPS isolation system with numerous concave sliding interfaces without prior knowledge of detailed information on the mobilized forces at various sliding stages.An MFPS with numerous concave sliding interfaces and one articulated or rigid slider located between these interfaces is divided into two subsystems: the fi rst represents the concave sliding interfaces above the slider,and the second represents those below the slider.The equivalent series system for the entire system is then obtained by connecting those for each subsystem in series.The equivalent series system is validated by comparing numerical results for an MFPS with four sliding interfaces obtained from the proposed method with those from a previous study by Fenz and Constantinou.Furthermore,these numerical results demonstrate that an MFPS isolator with numerous concave sliding interfaces,which may have any number of sliding interfaces,is a good isolation device to protect structures from earthquake damage through appropriate designs with controllable mechanisms.

Hierarchical interacting multiple model algorithm based on improved current model

Interacting multiple models is the hotspot in the research of maneuvering target models at present. A hierarchical idea is introduced into IMM algorithm. The method is that the whole models are organized as two levels to co-work, and each cell model is an improved ＂current＂ statistical model. In the improved model, a kind of nonlinear fuzzy membership function is presented to get over the limitation of original model, which can not track weak maneuvering target precisely. At last, simulation experiments prove the efficient of the novel algorithm compared to interacting multiple model and hierarchical interacting multiple model based original ＂current＂ statistical model in tracking precision.

Multiple Measurement Models of Articulated Arm Coordinate Measuring Machines

The existing articulated arm coordinate measuring machines（AACMM） with one measurement model are easy to cause low measurement accuracy because the whole sampling space is much bigger than the result in the unstable calibration parameters. To compensate for the deficiency of one measurement model, the multiple measurement models are built by the Denavit-Hartenberg＇s notation, the homemade standard rod components are used as a calibration tool and the Levenberg-Marquardt calibration algorithm is applied to solve the structural parameters in the measurement models. During the tests of multiple measurement models, the sample areas are selected in two situations. It is found that the measurement errors＇ sigma value（0.083 4 ram） dealt with one measurement model is nearly two times larger than that of the multiple measurement models（0.043 1 ram） in the same sample area. While in the different sample area, the measurement errors＇ sigma value（0.054 0 ram） dealt with the multiple measurement models is about 40% of one measurement model（0.137 3 mm）. The preliminary results suggest that the measurement accuracy of AACMM dealt with multiple measurement models is superior to the accuracy of the existing machine with one measurement model. This paper proposes the multiple measurement models to improve the measurement accuracy of AACMM without increasing any hardware cost.

MicroRNAs as disease progression biomarkers and therapeutic targets in experimental autoimmune encephalomyelitis model of multiple sclerosis

Multiple sclerosis is an autoimmune neurodegenerative disease of the central nervous system characterized by pronounced inflammatory infiltrates entering the brain,spinal cord and optic nerve leading to demyelination.Focal demyelination is associated with relapsing-remitting multiple sclerosis,while progressive forms of the disease show axonal degeneration and neuronal loss.The tests currently used in the clinical diagnosis and management of multiple sclerosis have limitations due to specificity and sensitivity.MicroRNAs(miRNAs)are dysregulated in many diseases and disorders including demyelinating and neuroinflammatory diseases.A review of recent studies with the experimental autoimmune encephalomyelitis animal model(mostly female mice 6–12 weeks of age)has confirmed miRNAs as biomarkers of experimental autoimmune encephalomyelitis disease and importantly at the pre-onset(asymptomatic)stage when assessed in blood plasma and urine exosomes,and spinal cord tissue.The expression of certain miRNAs was also dysregulated at the onset and peak of disease in blood plasma and urine exosomes,brain and spinal cord tissue,and at the post-peak(chronic)stage of experimental autoimmune encephalomyelitis disease in spinal cord tissue.Therapies using miRNA mimics or inhibitors were found to delay the induction and alleviate the severity of experimental autoimmune encephalomyelitis disease.Interestingly,experimental autoimmune encephalomyelitis disease severity was reduced by overexpression of miR-146a,miR-23b,miR-497,miR-26a,and miR-20b,or by suppression of miR-182,miR-181c,miR-223,miR-155,and miR-873.Further studies are warranted on determining more fully miRNA profiles in blood plasma and urine exosomes of experimental autoimmune encephalomyelitis animals since they could serve as biomarkers of asymptomatic multiple sclerosis and disease course.Additionally,studies should be performed with male mice of a similar age,and with aged male and female mice.

Predicting the growth performance of growing-finishing pigs based on net energy and digestible lysine intake using multiple regression and artificial neural networks models

Backgrounds:Evaluating the growth performance of pigs in real-time is laborious and expensive,thus mathematical models based on easily accessible variables are developed.Multiple regression(MR)is the most widely used tool to build prediction models in swine nutrition,while the artificial neural networks(ANN)model is reported to be more accurate than MR model in prediction performance.Therefore,the potential of ANN models in predicting the growth performance of pigs was evaluated and compared with MR models in this study.Results:Body weight(BW),net energy(NE)intake,standardized ileal digestible lysine(SID Lys)intake,and their quadratic terms were selected as input variables to predict ADG and F/G among 10 candidate variables.In the training phase,MR models showed high accuracy in both ADG and F/G prediction(R^(2)_(ADG)=0.929,R^(2)_(F/G)=0.886)while ANN models with 4,6 neurons and radial basis activation function yielded the best performance in ADG and F/G prediction(R^(2)_(ADG)=0.964,R^(2)_(F/G)=0.932).In the testing phase,these ANN models showed better accuracy in ADG prediction(CCC:0.976 vs.0.861,R^(2):0.951 vs.0.584),and F/G prediction(CCC:0.952 vs.0.900,R^(2):0.905 vs.0.821)compared with the MR models.Meanwhile,the“over-fitting”occurred in MR models but not in ANN models.On validation data from the animal trial,ANN models exhibited superiority over MR models in both ADG and F/G prediction(P<0.01).Moreover,the growth stages have a significant effect on the prediction accuracy of the models.Conclusion:Body weight,NE intake and SID Lys intake can be used as input variables to predict the growth performance of growing-finishing pigs,with trained ANN models are more flexible and accurate than MR models.Therefore,it is promising to use ANN models in related swine nutrition studies in the future.

Animal model of non-bacterial multiple organ dysfunction syndrome in the elderly

Objective To establish a model of multiple organ dysfunction syndrome in the elderly (MODSE) by intraperitoneal injection of different doses of zymosan, and to compare the multiple organ dysfunction syndrome (MODS) in adult and in the elderly rats. Methods Adult and senile rats, injected with different doses of zymosan intraperitoneally were examined for the changes in the function and morphology of the vital organs, including heart, liver, brain, lungs, and kidneys using blood gas and biochemistry analysis and histopathological examination methods. Results Compared with the normal controls of the adult and the elderly rats, the blood gas and blood biochemistry changed in different degrees in the different dosed zymosan groups. Pathological changes were also found in the vital organs including lungs, heart, liver, brain, kidneys, erc in the experimental groups. Under the same concentrations of zymosan, the reductions in respiratory, cardiac and renal functions in the senile groups were much more severe than those in the corresponding adult group. In the similar degree of model duplication, the senile rats had the tendency to die later than the adult rats. Conclusions Zymosan can be used in both elderly and adult rats to induce MODS model, and the best dosage for MODSE was 0.Sg/kg injected peritoneally. The model would hopefully be used in the study of mechanisms and the therapeutics on MODSE.

Multiple model tracking algorithms based on neural network and multiple process noise soft switching

A multiple model tracking algorithm based on neural network and multiple-process noise soft-switching for maneuvering targets is presented.In this algorithm, the＂current＂statistical model and neural network are running in parallel.The neural network algorithm is used to modify the adaptive noise filtering algorithm based on the mean value and variance of the＂current＂statistical model for maneuvering targets, and then the multiple model tracking algorithm of the multiple processing switch is used to improve the precision of tracking maneuvering targets.The modified algorithm is proved to be effective by simulation.

Protective effects of pharmacological therapies in animal models of multiple sclerosis: a review of studies 2014–2019

Multiple sclerosis(MS)is an inflammatory demyelinating disease of the central nervous system.The disability caused by inflammatory demyelination clinically dominates the early stages of relapsing-remitting MS and is reversible.Once there is considerable loss of axons,MS patients enter a secondary progressive stage.Disease-modifying drugs currently in use for MS suppress the immune system and reduce relapse rates but are not effective in the progressive stage.Various animal models of MS(mostly mouse and rat)have been established and proved useful in studying the disease process and response to therapy.The experimental autoimmune encephalomyelitis animal studies reviewed here showed that a chronic progressive disease can be induced by immunization with appropriate amounts of myelin oligodendrocyte glycoprotein together with mycobacterium tuberculosis and pertussis toxin in Freund's adjuvant.The clinical manifestations of autoimmune encephalomyelitis disease were prevented or reduced by treatment with certain pharmacological agents given prior to,at,or after peak disease,and the agents had protective effects as shown by inhibiting demyelination and damage to neurons,axons and oligodendrocytes.In the cuprizone-induced toxicity animal studies,the pharmacological agents tested were able to promote remyelination and increase the number of oligodendrocytes when administered therapeutically or prophylactically.A monoclonal IgM antibody protected axons in the spinal cord and preserved motor function in animals inoculated with Theiler's murine encephalomyelitis virus.In all these studies the pharmacological agents were administered singly.A combination therapy may be more effective,especially using agents that target neuroinflammation and neurodegeneration,as they may exert synergistic actions.

Aircraft Trajectory Prediction Based on Modified Interacting Multiple Model Algorithm

In order to realize the aircraft trajectory prediction,a modified interacting multiple model(M-IMM) algorithm is proposed,which is based on the performance analysis of the standard interacting multiple model(IMM) algorithm.In the proposed M-IMM algorithm,a new likelihood function is defined for the sake of updating flight mode probabilities,in which the influences of interacting to residual's mean error are taken into account and the assumption of likelihood function being a zero mean Gaussian function is discarded.Finally,the proposed M-IMM algorithm is applied to the simulation of the aircraft trajectory prediction,and the comparative studies are conducted to existing algorithms.The simulation results indicate the proposed M-IMM algorithm can predict aircraft trajectory more quickly and accurately.

Multiple model efficient particle filter based track-before-detect for maneuvering weak targets

It is a tough problem to jointly detect and track a weak target, and it becomes even more challenging when the target is maneuvering. The above problem is formulated by using the Bayesian theory and a multiple model(MM) based filter is proposed. The filter presented uses the MM method to accommodate the multiple motions that a maneuvering target may travel under by adding a random variable representing the motion model to the target state. To strengthen the efficiency performance of the filter,the target existence variable is separated from the target state and the existence probability is calculated in a more efficient way. To examine the performance of the MM based approach, a typical track-before-detect(TBD) scenario with a maneuvering target is used for simulations. The simulation results indicate that the MM based filter proposed has a good performance in joint detecting and tracking of a weak and maneuvering target, and it is more efficient than the general MM method.

Breakdown voltage model and structure realization of a thin silicon layer with linear variable doping on a silicon on insulator high voltage device with multiple step field plates

Based on the theoretical and experimental investigation of a thin silicon layer（TSL） with linear variable doping（LVD） and further research on the TSL LVD with a multiple step field plate（MSFP）,a breakdown voltage（BV） model is proposed and experimentally verified in this paper.With the two-dimensional Poisson equation of the silicon on insulator（SOI） device,the lateral electric field in drift region of the thin silicon layer is assumed to be constant.For the SOI device with LVD in the thin silicon layer,the dependence of the BV on impurity concentration under the drain is investigated by an enhanced dielectric layer field（ENDIF）,from which the reduced surface field（RESURF） condition is deduced.The drain in the centre of the device has a good self-isolation effect,but the problem of the high voltage interconnection（HVI） line will become serious.The two step field plates including the source field plate and gate field plate can be adopted to shield the HVI adverse effect on the device.Based on this model,the TSL LVD SOI n-channel lateral double-diffused MOSFET（nLDMOS） with MSFP is realized.The experimental breakdown voltage（BV） and specific on-resistance（R on,sp） of the TSL LVD SOI device are 694 V and 21.3 ·mm 2 with a drift region length of 60 μm,buried oxide layer of 3 μm,and silicon layer of 0.15 μm,respectively.